Actualizar app.py

app.py

@@ -2,33 +2,167 @@ import gradio as gr
 import requests
 import json
 import os
-
+import random
+from typing import List, Optional
+
+# Tu clase generadora de prompts
+class HyperrealisticPromptGenerator:
+    def __init__(self):
+        self.ROLES = [
+            "nurse", "nun", "maid", "flight attendant", "secretary", "teacher", "schoolgirl", "lawyer",
+            "doctor", "boudoir model", "fitness model", "elegant judge", "seductive librarian",
+            "business executive", "policewoman", "female military officer", "WWII-era secretary",
+            "1960s flight attendant", "seductive maid", "mysterious nurse", "captivating schoolgirl"
+        ]
+        self.AGES = [
+            "early 20s youthful vibrance",
+            "early 20s fresh and vibrant",
+            "mid 20s graceful confidence",
+            "mid 20s elegant and fresh",
+            "early 20s natural glow"
+        ]
+        self.HAIR_COLORS = [
+            "deep sapphire blue", "silver platinum", "vibrant ruby red", "glossy jet black",
+            "luxurious chestnut brown", "emerald green", "vivid amethyst purple",
+            "chocolate brown", "honey blonde", "burgundy red"
+        ]
+        self.EYE_COLORS = [
+            "intense brown", "bright sapphire blue", "emerald green", "golden amber",
+            "fascinating hazel", "deep violet", "piercing emerald", "mysterious gray",
+            "vibrant violet", "intense amber"
+        ]
+        self.HAIR_STYLES = [
+            "long flowing chestnut hair styled in soft waves",
+            "sleek straight long black hair",
+            "luxurious long blonde curls",
+            "elegant updo with loose cascading strands",
+            "glossy long brunette hair parted in the middle",
+            "voluminous curls",
+            "thick braid over the shoulder",
+            "loose and silky layers",
+            "messy chic bun"
+        ]
+        self.POSES = [
+            "standing with one leg slightly forward, skirt shifting gently to subtly reveal lace thong, view from knees to head",
+            "seated on a chair edge, legs crossed, skirt moving slightly, natural sensual expression, low angle from knees",
+            "leaning against a desk with hips cocked, skirt riding up, captured from knees to head",
+            "walking with natural sway, skirt flowing, viewed contrapicado from knees",
+            "adjusting stockings or shoes, skirt slightly lifted revealing thong, viewed low angle knees up"
+        ]
+        self.SETTINGS = [
+            "modern office with elegant decor and warm ambient light",
+            "luxury hotel suite with velvet furnishings and city view",
+            "classic library with wooden shelves and soft reading lamps",
+            "outdoor balcony at sunset with urban skyline",
+            "high-end photo studio with professional soft lighting"
+        ]
+        self.ATMOSPHERES = [
+            "soft professional lighting with smooth skin shadows, perfect color balance",
+            "warm golden hour sunlight creating rich highlights and depth",
+            "moody cinematic lighting with subtle shadow play",
+            "gentle romantic candlelight with warm glows",
+            "sharp studio flash lighting with balanced illumination"
+        ]
+        self.TECHNICAL_DETAILS = (
+            "Captured in ultra HD 16K (15360×8640) vertical 9:16 full body format. "
+            "Canon EOS R5 Cine RAW camera and Canon RF 85mm f/1.2L USM lens at f/1.2 aperture for creamy bokeh and realistic depth of field. "
+            "ARRI SkyPanel S360-C with soft shadowless 3:1 lighting ratio. "
+            "Advanced Path Tracing, Physically Based Rendering (PBR), Subsurface Scattering (SSS) for lifelike skin translucency, "
+            "Ray Tracing for global illumination and reflections. "
+            "Photogrammetry-based texture mapping, displacement maps for skin pores, delicate fabric weave and lace micro-details. "
+            "Natural, physics-driven hair strand flow. "
+            "Composition uses contrapicado low-angle (knee to head) shots emphasizing natural, sensual lingerie reveal."
+        )
+        self.CONDITION_FIXED = (
+            "Wearing elegant thigh-high stockings, no bra, and high stilettos. "
+            "Delicately revealing a lace thong in a natural, seductive manner, as if caught candidly. "
+            "Age between 20 and 25, radiating youthfulness and fresh allure. "
+            "Pose and framing strictly low-angle, knees to head vertical 9:16 aspect ratio, full body filling the frame."
+        )
+
+    def _choose_random(self, options: List[str]) -> str:
+        return random.choice(options)
+
+    def generate_single_prompt(self, role: Optional[str] = None) -> str:
+        selected_role = role if role else self._choose_random(self.ROLES)
+        age = self._choose_random(self.AGES)
+        hair_color = self._choose_random(self.HAIR_COLORS)
+        eye_color = self._choose_random(self.EYE_COLORS)
+        hair_style = self._choose_random(self.HAIR_STYLES)
+        pose = self._choose_random(self.POSES)
+        setting = self._choose_random(self.SETTINGS)
+        atmosphere = self._choose_random(self.ATMOSPHERES)
+        prompt = (
+            f"```
+            f"Age: {age}\n"
+            f"Hair: {hair_style} in {hair_color}\n"
+            f"Eyes: {eye_color} with expressive, captivating gaze\n"
+            f"Pose: {pose}\n"
+            f"Environment: {setting}\n"
+            f"Atmosphere: {atmosphere}\n"
+            f"Outfit: {self.CONDITION_FIXED}\n\n"
+            f"Technical specs:\n{self.TECHNICAL_DETAILS}\n```"
+        )
+        return prompt
+
+    def generate_prompt_automatic(self):
+        # Simple método para generación automática sin rol pasado
+        return self.generate_single_prompt()
+
+# Instancia del generador
+gen = HyperrealisticPromptGenerator()
+
+# Configuración API SambaNova
 API_KEY = os.getenv("SAMBANOVA_API_KEY")
 API_URL = "https://api.sambanova.ai/v1/chat/completions"
-
 headers = {
     "Authorization": f"Bearer {API_KEY}",
     "Content-Type": "application/json",
 }
 
-def query_sambanova_with_image(user_message, image_base64, chat_history):
+def analizar_imagen_y_generar_prompt(image_url_or_base64):
+    # Se envía la imagen para obtener una descripción en inglés
+    messages = [
+        {"role": "system", "content": "You are an assistant that describes images in detailed English."},
+        {"role": "user", "content": [
+            {"type": "image_url", "image_url": {"url": image_url_or_base64}},
+            {"type": "text", "text": "Provide a detailed English description of this image to use as a prompt."},
+        ]}
+    ]
+    json_data = {
+        "model": "Llama-4-Maverick-17B-128E-Instruct",
+        "messages": messages,
+        "stream": False,
+    }
+    try:
+        response = requests.post(API_URL, headers=headers, json=json_data)
+        response.raise_for_status()
+        resp_json = response.json()
+        text_resp = resp_json["choices"][0]["message"]["content"]
+        prompt = f"``````"
+        return prompt
+    except Exception as e:
+        return f"Error al analizar imagen y generar prompt: {e}"
+
+def chat_sambanova(user_message, image_input, auto_mode, chat_history):
+    updated_history = chat_history.copy()
+
+    if auto_mode and image_input:
+        # Modo automático: analizar imagen y generar prompt automáticamente
+        prompt = analizar_imagen_y_generar_prompt(image_input)
+        # Mostrar prompt generado en el chat
+        updated_history.append(("IA - Prompt automático generado:", prompt))
+        return "", updated_history
+
+    # Modo chat manual: enviar mensaje a SambaNova API
     messages = [{"role": "system", "content": "Eres un asistente útil"}]
-    for user_msg, ai_msg in chat_history:
+    for user_msg, ai_msg in updated_history:
         messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]})
         messages.append({"role": "assistant", "content": ai_msg})
 
-    # Construir contenido del mensaje con texto e imagen
-    user_content = [
-        {"type": "text", "text": user_message}
-    ]
-    if image_base64:
-        user_content.append({
-            "type": "image_url",
-            "image_url": {
-                "url": image_base64
-            }
-        })
-
+    user_content = [{"type": "text", "text": user_message}]
+    if image_input:
+        user_content.append({"type": "image_url", "image_url": {"url": image_input}})
     messages.append({"role": "user", "content": user_content})
 
     json_data = {
@@ -42,7 +176,6 @@ def query_sambanova_with_image(user_message, image_base64, chat_history):
         response.raise_for_status()
 
         collected_text = ""
-        updated_history = chat_history.copy()
         updated_history.append((user_message, ""))
 
         for line in response.iter_lines(decode_unicode=True):
@@ -61,27 +194,31 @@ def query_sambanova_with_image(user_message, image_base64, chat_history):
                         if updated_history:
                             updated_history[-1] = (updated_history[-1][0], collected_text)
 
-                        yield updated_history, updated_history
+                        yield "", updated_history
                     except json.JSONDecodeError:
                         continue
     except requests.exceptions.RequestException as err:
         error_msg = f"Error: {err}"
-        updated_history = chat_history.copy()
         if not updated_history or updated_history[-1][0] != user_message:
             updated_history.append((user_message, error_msg))
         else:
             updated_history[-1] = (updated_history[-1][0], error_msg)
-        yield updated_history, updated_history
+        yield "", updated_history
 
+def generar_prompt_interno():
+    return gen.generate_prompt_automatic()
 
 with gr.Blocks() as demo:
     chat_history = gr.State([])
-    chatbot = gr.Chatbot()
+    chatbot = gr.Chatbot(label="Chatbot IA")
     msg = gr.Textbox(label="Escribe tu mensaje")
-    img_input = gr.Textbox(label="Imagen base64 o URL (opcional)", placeholder="Pega aquí la imagen en base64 o URL")
-    btn = gr.Button("Enviar")
+    img_input = gr.Textbox(label="Imagen base64 o URL (opcional)", placeholder="Pega aquí la imagen o URL")
+    auto_mode = gr.Checkbox(label="Modo automático (generar prompt auto desde imagen)", value=False)
+    btn_send = gr.Button("Enviar mensaje")
+    btn_gen_prompt = gr.Button("Generar prompt automático interno")
 
-    btn.click(query_sambanova_with_image, inputs=[msg, img_input, chat_history], outputs=[chatbot, chat_history])
-    msg.submit(query_sambanova_with_image, inputs=[msg, img_input, chat_history], outputs=[chatbot, chat_history])
+    btn_send.click(chat_sambanova, inputs=[msg, img_input, auto_mode, chat_history], outputs=[msg, chatbot, chat_history])
+    msg.submit(chat_sambanova, inputs=[msg, img_input, auto_mode, chat_history], outputs=[msg, chatbot, chat_history])
 
-demo.launch()
+    btn_gen_prompt.click(lambda: (gen.generate_prompt_
+